Code Scanner

Comprehensive static code analysis to identify potential issues, vulnerabilities, and areas for improvement in your codebase.

Key Features

1. Static Analysis: The feature performs in-depth analysis of your code to identify potential issues, vulnerabilities, and areas for improvement.

2. Multi-language Support: Code Scanner can analyze code in various programming languages and frameworks.

3. Detailed Reporting: Generates comprehensive reports highlighting issues, potential bugs, and suggestions for improvement.

4. Security Vulnerability Detection: Identifies potential security vulnerabilities in the codebase.

5. Code Quality Metrics: Provides metrics and insights on code quality and maintainability.

How it Works

• Input your source code or provide access to your repository.
• The Code Scanner employs advanced algorithms and AI to analyze the code and generate a detailed report of findings.
• The scanner identifies potential issues, vulnerabilities, and areas for improvement in your codebase.

You can find an example here (opens in a new tab)

Usage Guide

Here's a detailed step-by-step guide on how to use the Code Scanner:

1. Follow the initial setup guide here.

2. Create a new Python file and import the necessary modules from Kaizen:

    from kaizen.reviewer.code_scan import CodeScanner
    from kaizen.llms.provider import LLMProvider
    import json

3. Create an instance of the CodeScanner:

   reviewer = CodeScanner(llm_provider=LLMProvider())

4. Provide the source code file or directory path which you want to scan:

   You can scan a single file at a time:

    filename = "path/to/your/file.py"
    with open(filename, "r+") as f:
        file_data = f.read()
    review_data = reviewer.review_code(file_data=file_data, user="YourUsername/ProjectName")

   Or a whole directory:

    dir_path = "path/to/your/directory/"
    review_data = reviewer.review_code_dir(
        dir_path=dir_path, reevaluate=True, user="YourUsername/ProjectName"
    )

5. Display the test results:

    print(f"Total {len(review_data.issues)} Issues found!!!!")
    print(json.dumps(review_data.issues, indent=2))

6. Review the generated report and address the identified issues in your code.

Example

Here's a complete example to generate unit tests for a specific file:

    from kaizen.reviewer.code_scan import CodeScanner
    from kaizen.llms.provider import LLMProvider
    import json
 
    # Create an instance of the CodeScanner
    reviewer = CodeScanner(llm_provider=LLMProvider())
 
    # Scan a single file
    filename = "github_app/main.py"
    with open(filename, "r+") as f:
        file_data = f.read()
    review_data = reviewer.review_code(file_data=file_data, user="Example/CodeScan")
 
    # Scan a whole directory
    dir_path = "github_app/"
    review_data = reviewer.review_code_dir(
        dir_path=dir_path, reevaluate=True, user="Example/CodeScan"
    )
 
    # Display the results
    print(f"Total {len(review_data.issues)} Issues found!!!!")
    print(json.dumps(review_data.issues, indent=2))

Supported Languages

• All programming languages

Benefits

• Early Bug Detection
• Security Vulnerability Identification
• Code Quality Improvement
• Coding Standard Enforcement
• Technical Debt Reduction

Limitations

• False Positives: Some identified issues may not be actual problems in certain contexts.
• Language Coverage: Effectiveness may vary depending on programming language and framework.
• AI Limitations: May not catch all possible issues or understand complex project-specific requirements.